Dynamic Multi-Access Wireless Network Virtualization

ABSTRACT

We disclose systems and methods of dynamically virtualizing a wireless communication network. The communication network is comprised of heterogeneous multi-RAT mesh nodes coupled to a computing cloud component. The computing cloud component virtualizes the true extent of the resources it manages and presents an interface to the core network that appears to be a single base station.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/705,440, entitled “Multi-Access and Backhaul Wireless Systems andMethods” filed on Sep. 25, 2012; and to U.S. Provisional PatentApplication No. 61/718,503 entitled “Software Defined NetworkingApproach for Wireless Base Station with Backhaul,” filed on Oct. 25,2012; and to U.S. Provisional Patent Application No. 61/724,312 entitled“Method of Optimizing Paging over LTE Radio,” filed on Nov. 9, 2012; andto U.S. Provisional Patent Application No. 61/724,963 entitled “MultiAccess Wireless Systems Virtualization Methods,” filed on Nov. 10, 2012;and to U.S. Provisional Patent Application No. 61/724,964 entitled“Multi Access Wireless Systems Virtualization Methods,” filed on Nov.10, 2012; and to U.S. Provisional Patent Application No. 61/765,729,entitled “Situation Aware Mobile Wireless Base Station for FirstResponders,” filed on Feb. 17, 2013, the entire contents of which arehereby incorporated by reference.

FIELD

The present application relates to wireless broadband communicationsystems, cellular systems, small cells, and network virtualization.

BACKGROUND

The concept of virtualization can be defined broadly as a methodology bywhich an underlying resource is shared across multiple consumers, whileproviding each of the consumers with the illusion of owning the entireresource independent from the other consumers. Virtualization can beapplied across both wired and wireless networks. In the wireless realm,virtualization has been done on IP networks and in wirelesscommunication networks. Even within these two categories, there arevarious types of virtualization. For example, for IP networks, U.S.Patent Publication No. 2013/0094486 entitled “Wireless NetworkVirtualization for Wireless Local Area Networks” describes an accesspoint for air-time guarantees to a group of clients who share an accesspoint.

In another IP network patent application, WO 2011/144538 A1 entitled“Method and System for Network Virtualization,” the inventors discusssharing a common network infrastructure by splitting it into severallogical network instances, called “slices,” composed of virtual node“slivers” and virtual links. The application describes how to isolatewireless resources coexisting at the same time to ensure minimalinterference among the resources as well as controlling wirelessresource utilization to ensure that one slice does not infringe on theresources of other slices.

As for wireless communication networks, U.S. Patent Application No.2009/0170472 entitled “Shared Network Infrastructure” discloses awireless communication network where “[v]irtualization can provide anabstraction layer that allows multiple virtual machines to execute inisolation from one another, side-by-side on the same physical machine.”¶69.

Although these versions of virtualization differ in the medium in whichthey are implemented, they nonetheless share the common point that theyare a “one-to-many” type of virtualization. By “one-to-many,” we mean acommon set of hardware is shared by more than one client or subscriber.In a way, this type of arrangement is like multi-tenancy in abuilding—many people living in individual apartments within a singlebuilding infrastructure.

These prior are applications further embody multi-tenancy in the sensethat the people who live within a certain building are always allocatedthe same amount of living space. For example, a tenant in amulti-tenancy building may rent a one bedroom apartment. If at somepoint in time, he has six relatives visiting from afar, he is still onlyallocated a one bedroom apartment. His increased demand for living spaceis of no moment in this model. As a result, his relatives will have tosleep on couches, the floor, wherever they can find space because thenature of a multi-tenancy model is static. It does not respond tochanging environmental conditions, and there is no intelligence in areal-time sense associated with the allocation of resources.

To the extent that the prior art virtualization techniques encompassintelligent, real-time decision making, they most typically reportalarms experienced within the system. These alarms alert clients withinthe network that network performance may be jeopardized, but they do notmake dynamic decisions about how to respond to the alarm. In terms ofpooled resources, some prior art virtualization techniques include theidea of pooled processing capabilities. But they do not include the ideaof pooled resources. In the case of a heterogeneous, multi-RAT meshnetwork, there can be hundreds of pooled resources available for usedepending upon network conditions.

Going back to the definition of virtualization, each of these “many”users believe that they have exclusive use and control of the one set ofhardware. FIG. 1 is an example of how a prior art virtualized basestation 100 may operate according to this definition of virtualization.The base station 100 is comprised of a single set of hardwarecomponents. Additionally, the prior art performs virtualization in aphysical way, on the hardware. On that single set of hardwarecomponents, virtualization techniques are used such that AT&T andVerizon could unknowingly share the hardware components of this basestation, each believing that it was the only network operator using thebase station 100.

While this type of virtualization has the advantage of being able toefficiently utilize common hardware, it is a one-way street in terms ofthe perspective from which you view virtualization. Specifically, theeach service provider sees the base station 100 as being dedicated to itexclusively. In that respect, the base station 100 is virtualized whenlooking from the core network toward the base station. In the oppositedirection, however, from the base station 100 to the core network, thereis no virtualization. And that is in some respects a function of thestatic nature of prior art virtualization. Resources on the base station100 are statically partitioned in a multi-tenancy fashion, which meansthat any change in the network, e.g., capacity, operating conditions,latency experienced by one network operator, cannot be dynamicallyaddressed. There is a need, therefore, to create a dynamic wirelesscommunication network.

This need is felt in many different types of wireless communicationnetworks. Historically, wireless communication has been performed on 3Gor Wi-Fi networks using macro cells and access points for local Wi-Fidata delivery. Looking forward, the selection of networks available forwireless communication is increasing to include LTE, TV White Space,small cell solutions integrated within macro networks, and so forth.Base stations that support this heterogeneous network, which is anetwork that integrates multiple radio technologies, will require moresophisticated management techniques in order to handle theever-increasing demands being placed on networks.

Focusing for example on LTE networks, LTE has been designed to supportonly packet-switched services, in contrast to the circuit-switched modelof previous cellular systems. One of the goals of LTE is to provideseamless Internet Protocol (IP) connectivity between user equipment (UE)and the packet data network (PDN), without any disruption to the endusers' applications during mobility. See generally “The LTE NetworkArchitecture: A comprehensive tutorial,” Strategic White Paper,Alcatel-Lucent.

While the term “LTE” encompasses the evolution of the Universal MobileTelecommunications System (UMTS) radio access through the Evolved UTRAN(E-UTRAN), it is accompanied by an evolution of the non-radio aspectsunder the term “System Architecture Evolution” (SAE), which includes theEvolved Packet Core (EPC) network. Together LTE and SAE comprise theEvolved Packet System (EPS). Id.

EPS uses the concept of EPS bearers to route IP traffic from a gatewayin the PDN to the UE. A bearer is an IP packet flow with a definedquality of service (QoS) between the gateway and the UE. Together, theE-UTRAN and EPC set up and release bearers as required by applications.Id. An EPS bearer is typically associated with a QoS. Multiple bearerscan be established for a user in order to provide different QoS streamsor connectivity to different PDNs. For example, a user might be engagedin a voice (VoIP) call, while at the same time performing web browsingor an FTP download. A VoIP bearer would provide the necessary QoS forthe voice call, while a best-effort bearer would be suitable for the webbrowsing or FTP session. Id.

FIG. 2 shows the overall network architecture, including the networkelements and the standardized interfaces. The core network (called EPCin SAE) is responsible for the overall control of the UE andestablishment of the bearers. The main logical nodes of the EPC are: (1)PDN Gateway (P-GW); (2) Serving Gateway (S-GW); and (3) MobilityManagement Entity (MME). Currently, wireless base stations, such as LTEeNodeB and its backhaul networking infrastructure are managed on acomponent by component basis.

As an example, if one eNodeB used three different microwave backhaullinks, each would be dedicated to, and managed by, a different vendor.As a result, there would be three distinct sets of operator policies andnetworking policies that would have to be translated to three distinctconfigurations unique to the individual pieces of backhaul equipment.Similarly, on the access side, there are four major network operators inthe US: AT&T, Verizon, Sprint, and T-Mobile. Each of these networkoperators deploys its own proprietary architecture similar to that shownin FIG. 2, but importantly, lacking in interoperability between thecarriers. The term “eNodeB” is used within the art to denote a standard,as opposed to uncustomized LTE base station. We use this term throughoutto have that meaning, which is distinct form our customized multi-RATnodes. Our multi-RAT nodes can function as standardized LTE basestations, but they also have much wider functionality

Packet core signaling volumes in the early deployments of large-scaleLTE networks are significantly higher than in existing 2G/3G corenetworks. This is partly due to the flatter, all-IP architecture of LTEwhere the macro and metro cell is directly connected to the MME—thededicated control plane element in the EPC. Analysis of field data fromseveral large LTE network deployments found that an MME can experience asustained signaling load of over 500-800 messages per user equipment(UE) during the normal peak busy hours and up to 1500 messages per userper hour under adverse conditions. See generally “Managing LTE CoreNetwork Signaling Traffic,” Jul. 30, 2013, Alcatel Lucent,www2.alcatel-lucent.com/techzine/managing-lte-core-network-signaling-traffic.

The rise in core signaling can also be attributed to an overall increasein network usage by LTE subscribers. In some large US metropolitanmarkets where LTE is available, network peak usage is as high as 45service requests per UE per hour in peak busy hours. As LTE grows inpopularity, signaling in the EPC will continue to rise, which increasesthe potential for control plane congestion and signaling storms if notproperly managed. Id. Additionally, when small cells, which are becomingmore ubiquitous, are added to the network, the EPC is called upon tomanage 100 to 1000 times more cells. Some of the functions that the EPChas to manage for each small cell include: (1) providing backhaul links;(2) dynamically configuration; (3) power level management; (4) physicalcell ID allocation; (5) signal management; and (6) increased handoverswithin the network because of the smaller transmit range of the smallcells. Therefore, MNOs need to deploy a carrier-grade, next-generationMME/Serving GPRS Support Nodes (SGSNs) platform that not only has thecapacity, scalability and CPU processing performance, but also thecapability to intelligently manage this traffic to reduce overall coresignaling.

Two examples of where signaling efficiencies can be gained by usingvirtualized networks are: (1) paging/tracking management procedures; and(2) handoff management. Turning first to paging/tracking, a UE goes intothe IDLE mode when its radio connection is released. When the UE is inIDLE mode and it needs to be reached by the network, for example if ithas an incoming call, LTE standards, as well as legacy standards, definea PAGING process for reaching the UE. Under these paging/trackingscenarios, a PAGE is sent on a control channel to all of the basestations in the last known tracking area for the UE.

When the core network was primarily composed of macro cells, there mayhave been 10 macros within a tracking area. In today's networks, thenumber of base stations within a tracking area can typically be 100 ormore. That is because service providers are augmenting their networks byincorporating small cells that ultimately connect to the core network sothey can keep pace with the increasing number of wireless communicationusers and the demands they put upon the network. Given the large numberof macro cells and small cells existing in today's networks, thetransmit cost to the EPC and the network generally can be very high ifthe EPC does not know exactly where the UE is at any given moment. Assmall cells become more integrated into existing networks, this transmitcost will only increase. At any given time within a particular trackingarea, there are many, many UEs that are in IDLE mode. A subset of thesewill require PAGING at any given instant. It is advantageous, therefore,to design a method and apparatus for efficiently managing networkresources with respect to PAGING.

Every eNodeB within the control of the EPC must coordinate with its EPCwhen it performs a handover of one of the UEs within its sector. Whensmall cells are a part of the network, the number of handovers amongthese small cells increases as compared with macro cells because thecoverage area for small cells is much less than for macros. Furthertaxing the system, each network operator has its own EPC. It is,therefore, desirable, from a network resources standpoint, toincorporate an aggregator node that could virtualize a portion, or allof the network so that, from the core network's perspective, it appearsto be servicing only one eNodeB when in fact behind that eNodeB is alarge network of multi-RAT nodes. The embodiments disclosed hereininclude an aggregator node, which we refer to as a “computing cloud.”

SUMMARY OF THE INVENTION

Embodiments of this invention include methods, systems, apparatuses, andarchitecture designed to provide wireless services by virtualizingseveral radio access technologies into a single architecture controlledby an intelligent radio controller. One embodiment discloses a pagingstrategy for a virtualized wireless communication network connected to apacket core network. An additional embodiment discloses a method ofhanding over using network attachment information.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a prior art virtualized base station.

FIG. 2 is an illustration of prior art LTE network architecture.

FIG. 3 is an illustration of a heterogeneous mesh network upon whichembodiments of the present invention can be executed upon.

FIG. 4 illustrates the internal architecture of a multi-RAT node.

FIG. 5 is an illustration of an architecture of a computing cloud inembodiments of the present invention having 3G, LTE and Wi-Fivirtualized resources.

FIG. 6 is an illustration of the two-way nature of the virtualizationsystems and methods disclosed herein.

FIG. 7 illustrates the two-way nature of the virtualization ofembodiments of this invention.

FIG. 8 illustrates a method of virtualizing a wireless communicationnetwork.

DEFINITIONS

The following definitions are included to supplement additionaldescriptions within the specification. These terms are to be interpretedas one skilled in the art would understand them.

Accelerator is a hardware device designed to speed up process, forexample a web accelerator is a proxy server that speeds up websiteaccess.

eNodeB is an abbreviation for an E-UTRAN Node B. An eNodeB is an elementin the E-UTRA, which is the air interface of 3GPP's Long Term Evolution“LTE.” An eNodeB interfaces with the UE and hosts the PHYsical (PHY),Medium Access Control (MAC), Radio Link Control (RLC), and Packet DataControl Protocol (PDCP) layers. It also hosts Radio Resource Control(RRC) functionality corresponding to the control plane. It performs manyfunctions including radio resource management, admission control,scheduling, enforcement of negotiated UL QoS, cell informationbroadcast, ciphering/deciphering of user and control plane data, andcompression/decompression of DL/UL user plane packet headers.

GTP-U is used for carrying user data within the general packet radioservice “GPRS” core network and between the radio access network and thecore network. The user data transported can be packets in any of IPv4,IPv6, or PPP formats. GTP-U protocol is used over S1-U, X2, S4, S5 andS8 interfaces of the Evolved Packet System (EPS). GTP-U Tunnels are usedto carry encapsulated T-PDUs and signaling messages between a given pairof GTP-U Tunnel Endpoints. The Tunnel Endpoint ID (TEID) which ispresent in the GTP header indicates which tunnel a particular T-PDUbelongs to. The transport bearer is identified by the GTP-U TEID and theIP address (source TEID, destination TEID, source IP address,destination IP address).

Heterogeneous mesh network means at least two dynamic mesh nodes capableof: using different protocols, or different duplexing schemes, oroperating in disparate frequency bands, or using differenttransmit/receive mediums, such as wired versus wireless. Differentprotocols may include Wi-Fi, 2G, 3G, 4G, WCDMA, LTE, LTE Advanced,ZigBee, or Bluetooth. Different duplexing schemes may include timedivision, code division, and frequency division schemes. Disparatefrequency bands may include so-called “whitespace,” VHF and UHFchannels, cellular telephony bands, public safety bands, and the like.

Iu proxy—a proxy is a server, which is a computer system or anapplication that acts like an intermediary for requests from clientsseeking resources form other servers. A client connects to the proxyserver, requesting some service, such as a file, connection, web page,or other resource available from a different server and the proxy serverevaluates the request as a way to simplify and control its complexity.The Iu interface is an external interface that connects the RNC to thecore network. An Iu proxy is therefore a proxy for the Iu interface.

MME is the acronym for mobility management entity. The MME is the keycontrol node for the LTE access network. It is responsible for trackingand paging procedure including retransmissions, and also for idle modeof User Equipment (UE). MME is also involved in bearer activation andits deactivation procedures, to its task also belongs choosing the SGWfor a UE in process of initial attach and when the intra-handover takeplace which involves Core Network (CN) node relocation. MME isresponsible for authenticating user towards the HSS, if user is roamingMME terminates S6a interface towards user's home HSS. All Non AccessStratum (NAS) signaling terminates at the MME point, which is alsoresponsible for generation and allocation of temporary UE identities(GUTI). Among its duties is also authorization UE to Public Land MobileNetwork (PLMN) and enforcing UE roaming restrictions if there are any.MME is also termination point of ciphering and integrity protection forNAS signaling. Lawful Interception (LI) of signaling could be alsosupported by MME entity. It also provides the control plane function formobility between LTE and 2G/3G networks by the S3 interface (from SGSNto MME). MME Functions include: NAS signaling; NAS signaling security;Inter CN node signaling for mobility between 3GPP access networks(terminating S3); UE Reach ability in ECM-IDLE, state (including controland execution of paging retransmission); Tracking Area list management;Mapping from UE location (e.g. TAI) to time zone, and signaling a UEtime zone change associated with mobility; PDN GW and Serving GWselection; MME selection for handovers with MME change; SGSN selectionfor handovers to 2G or 3G 3GPP access networks; Roaming (S6a, towardshome HSS); Authentication; Authorization; Bearer management functionsincluding dedicated bearer establishment; Lawful interception ofsignaling traffic; Warning message transfer function (includingselection of appropriate eNodeB); LB Reach ability procedures.

PGW is a packet data network “PDN” Gateway, and can also be called a PDNGW. The PGW provides connectivity to the UE to external packet datanetworks by being the point of exit and entry of traffic for the UE. AUE may have simultaneous connectivity with more than one PGW foraccessing multiple PDNs. The PGW performs policy enforcement, packetfiltering for each user, charging support, lawful Interception andpacket screening.

Proxy is defined in the traditional sense as a computer system or anapplication that acts like an intermediary for requests from clientsseeking resources form other servers. A client connects to the proxyserver, requesting some service, such as a file, connection, web page,or other resource available from a different server and the proxy serverevaluates the request as a way to simplify and control its complexity.We also use the term “proxy” to mean an aggregator or a back-to-backagent.

SDN controller is an application in software-defined networking (“SDN”)that manages flow control to enable intelligent networking. SDNcontrollers are based on protocols, such as OpenFlow, that allow serversto tell switches where to send packets. The controller is the core of anSDN network. It lies between network devices at one end and applicationsat the other end. Any communications between applications and deviceshave to go through the controller. The controller also uses protocolssuch as OpenFlow to configure network devices and choose the optimalnetwork path for application traffic.

SGW is the acronym for serving gateway. The SGW routes and forwards userdata packets, while also acting as the mobility anchor for the userplane during inter-eNodeB handovers and as the anchor for mobilitybetween LTE and other 3GPP technologies (terminating S4 interface andrelaying the traffic between 2G/3G systems and PDN GW).

S1-GW is an acronym for an S1-gateway. S1-GW is a proprietary gatewaythat aggregates eNodeB communications with the core network. In atypical architecture, each eNodeB could talk directly to the EPC. Inembodiments described herein, we aggregate those communications tovirtualize all eNodeBs or all base stations into a single eNodeB or basestation.

Upstream network device is an MME, SGW, or any hardware device that ispart of the core network.

WAG/MAG is a wireless access gateway/mobility access gateway. Thewireless access gateway (“WAG”) provides interconnection between accessand 3GPP networks. The WAG manages IP tunnels, QoS measurements, and itis responsible for roaming. The term TWAG, stands for trusted wirelessaccess gateway. The mobility access gateway (“MAG”) nodes provide themobility agent function for mobility management, managing mobility ofthe mobile terminal with respect to the local mobility anchor (“LMA”)and establishing and releasing a user-data transport tunnel for the IPaddress allocated by the Packet Data Network (“PDN”).

X2 handover is a handover that occurs across an X2 interface. An X2interface is a new type of interface introduced by the LTE Radio AccessNetwork. It connects neighboring eNodeBs in a peer-to-peer fashion toassist handover and provide a means for rapid coordination of radioresources.

DETAILED DESCRIPTION

The virtualization embodiments disclosed herein are comprised of methodsand computer-readable medium configured to execute these methods on aheterogeneous mesh wireless communication networks including a computingcloud as part of the network. Full details of the workings of theheterogeneous mesh network can be found in U.S. patent application Ser.No. 13/889,631 filed on May 8, 2013 entitled “Heterogeneous Mesh Networkand a Multi-RAT Node Used Therein,” the entire contents of which arehereby incorporated by reference. Of note, when we discuss aheterogeneous mesh network, it is to be understood that there could bean unmeshed backhaul link within the network. The unmeshed backhaul linkcould be comprised of one or more wired connections to the computingcloud or to another network device. Similarly, there could be unmeshedwireless backhaul links that are part of the heterogeneous mesh network.These variations are changes in the network topology that one of skillin the art would understand how to do and that would not change theinventiveness of the embodiments disclosed herein.

FIG. 3 depicts an exemplary heterogeneous mesh network 300 upon whichthe virtualization embodiments described herein can be executed. Theheterogeneous mesh network 300 is comprised of three multi-radio accesstechnology (“multi-RAT”) nodes 310, 320, 330 and a computing cloud 340component. By way of example, and without being limiting, each multi-RATnode 310, 320, 330 has an access radio and a backhaul radio. As can beenseen from FIG. 3, the heterogeneous mesh network 300 includes multi-RATnodes 310, 320, 330 with varied architecture. This varied architectureenables them to transmit and receive on myriad frequencies, protocols,duplexing schemes, and media access technologies. While each multi-RATnode 310, 320, 330 is depicted with only a single access radio and asingle backhaul radio, in additional embodiments, these multi-RAT nodes310, 320, 330 will have more than one access and/or backhaul radio. Thebackhaul connections 315, 317, 325 between the multi-RAT nodes 310, 320,330 can be wireless in the heterogeneous mesh network 300. In oneembodiment, multi-RAT node 330 can have a wired connection 335 to thecomputing cloud 340.

FIG. 4 illustrates the internal architecture of a multi-RAT node 400used in the present virtualization embodiments. Multi-RAT node 400 iscomprised of at least one processor 410, access hardware 420, backhaulhardware 430, an RF front-end 440, and a timing source 450. By way ofexample, the at least one processor 410 could contain firmware writtenin Linux. Additionally, the RF front-end 440 can be configured toprovide RF capabilities for multiple radio access technologies. In oneembodiment, the timing source could be GPS. Alternatively, the timingsource could be derived from the Ethernet, or an IEEE 1588 source, suchas SyncE, PTP/1588v2, and the like.

Referring again to FIG. 3, the computing cloud 340 component, or“computing cloud” for short, is comprised of software that is capable ofbeing run on any general purpose server. The computing cloud 340component, in some embodiments, can be a run on a secure, hosted cloudservice, such as those offered by Citrix or Amazon. The computing cloud340 component includes a memory for storing the computer readablemethods of the disclosed embodiments. The memory may also storeadditional network information, such as routing tables, environmentalconditions, operational parameters, and the like. The memory medium mayalso be a distributed memory medium, e.g., for security reasons, or inthe situation where more than once computing cloud 340 is a part of thecommunication network data may be distributed among memory mediumswithin each computing cloud 340 component or even within the memory ofmulti-RAT nodes 310, 320, 330 that are part of the network. Also, thememory medium may be one of the networks to which the current network iscoupled, e.g., a Storage Area Network, a Software Defined Networkcontroller and the like. The computing cloud is further comprised of aprocessor and a network interface that facilitates communication with atleast one multi-RAT node 310, 320, 330.

FIG. 5 illustrates a virtualized wireless communication network 500.This virtualized wireless communication network 500 includes multi-RATnodes 512, 514, 516 communicatively coupled to a computing cloud 530. Inthe embodiment shown in FIG. 5, one of the multi-RAT nodes 516 has awired connection 528 with the computing cloud, while another multi-RATnode 512 has a wireless connection 524 to the computing cloud. The thirdmulti-RAT node 514 is communicatively coupled to the computing cloud 530via its wireless connections 522, 526 to its neighbors, who are in-turndirectly connected to the computing cloud 530. Although FIG. 5 showsthree multi-RAT nodes 512, 514, 516 it is understood by those of skillin the art that a network could be created with as few as two multi-RATnodes. Similarly, the connections 522, 524, 526 between the multi-RATnodes 512, 514, 516 could be wired or wireless. As few as one multi-RATnode 512, 514, 516 within a network can be connected to the computingcloud 530 in these embodiments; and that connection could be wired orwireless, although a wired connection would be most reliable.

The computing cloud 530 is further comprised of a general purposeprocessor 532 and an accelerator 534. The processor 532 could be acentral processing unit. Additionally, while only one processor 532 andone accelerator 534 are shown, it is understood by those skilled in theart that there could be more of one, the other, or both in the computingcloud 530. The computing cloud 530 is communicatively coupled to a corenetwork 540, which believes that it is only connected to a singledevice, such as a single base station, rather than being connected tomore than one multi-RAT node 512, 514, 516 on the other side of thecomputing cloud 530.

It is to be understood that the general architecture of the wirelesscommunication network 500 is scalable and can be distributed because ofthe nature of the computing cloud 530. In terms of scalability, thewireless communication network 500 is scalable in at least two ways.First, although we show three multi-RAT nodes 512, 514, 516 coupled tothe computing cloud 530, this architecture could support hundreds,thousands, many thousands, and millions, of multi-RAT nodes withoutoverloading the core network.

Second, we show a single computing cloud 530. The computing cloud 530component is also scalable, meaning there could be numerous computingclouds 530. Each of these additional computing clouds 530 could containsimilar logic or the logic could be distributed. If the logic wasdistributed, the two or more computing clouds 530 could communicate witheach other via an XML-based protocol. Some of the types of informationthat could be shared via this protocol are, for example, environmentalconditions in each computing cloud's 530 respective network, theavailability of additional resources to be shared if needed, a queryfrom one computing cloud 530 to coordinate a handoff, and the like.

FIG. 6 shows a virtualized communication network 600 having exemplaryradio technologies of: 3G, Wi-Fi and LTE. The choice of these radiotechnologies is illustrative only and not intended to limit the scope ofthe disclosure herein. The general functionality of the virtualizedwireless communication network depicted in FIG. 6 is not intended to bedifferent in a general sense from the functionality depicted in FIG. 5.Rather FIG. 6 shows a more specific implementation of a virtualizedwireless communication network having three specific wirelesstechnologies within the network: 3G, LTE, and Wi-Fi. In a differentvirtualized wireless communication network having different radiotechnologies, the specific internal contents of the computing cloud 630would be altered to accommodate the wireless communication technologiesoperational within the network.

As can be seen from FIG. 6, the three multi-RAT nodes 612, 614, 616 arecommunicatively coupled to the computing cloud 630 via a wiredconnection from multi-RAT node 616 to the computing cloud 630. Inside ofthe computing cloud, there are specific processors and accelerators forLTE, shown as S1 proxy 633, Wi-Fi, shown as WAG/MAG 631, and 3G, shownas Iu proxy 632. The S1 proxy 633 in this embodiment is a softwareentity that virtualizes the existence of all of the LTE radios withinthe multi-RAT nodes 612, 614, 616 and facilitates handovers to and fromLTE. In an alternate embodiment, the S1 proxy 633 could be furthercustomized by adding proprietary extensions between the multi-RAT nodes612, 614, 616 and the S1 proxy 633. We call this embodiment of the S1proxy 633 an S1/S1′ proxy because on one side of the proxy, it would berelating to the core network 640 in a standard S1 fashion, but on theother side of the proxy it would be interacting with the LTE portion ofthe multi-RAT nodes 612, 614, 616 in a non-standard way.

The Iu proxy 632 is a software entity that virtualizes the 3G radios inthe multi-RAT nodes 612, 614, 616 and facilitates handovers to and from3G. The Wireless Access Gateway/Mobile Access Gateway “WAG/MAG” 631controls multiple access points, provides wireless connectivity, andfacilitates handovers between Wi-Fi networks and LTE networks for themulti-RAT nodes 612, 614, 616. In additional embodiments of the WAG/MAG631, it is possible that it could access data stored on the SIM card ofa UE being serviced by a multi-RAT node 612, 614, 616 in order toauthenticate the UE and allow it seamless connectivity to Wi-Fi accesspoints.

ePDG 634 is a standards defined interworking function between PDN anduntrusted non-3GPP standardized wireless interfaces such as Wi-Fi. FIG.6 also includes two gateways, an LTE gateway 635 and a 3G/Wi-Fi gateway636. These gateways 635, 636 are optional. In this embodiment, thegateways provide a connection to a local network 650 and a local contentserver 655. In the prior art, content and access to servers wastypically performed at a regional or national data center, which wastypically located a long distance away from most wireless networks. Inthis embodiment, we provide local network access 650 and a local contentserver 655 to reduce the traffic costs associated with accessing serversor content. In addition, local content could be shared with many userswithin a network. For example, local movie times, news, restaurants,social media, and the like could be available on local content server655.

In alternate embodiments using similar architecture, the computing cloud630 could contain a software module that allows it to performdeep-packet inspection. In these embodiments, the deep-packet inspectioncould be used to distinguish various data types, e.g., voice versusdata, or video versus voice. This distinction, which can also be a QoSdistinction, could be used to make radio bearer determinations, tochoose an available network, to change data priority, and the like.

In the prior art, there was no need for gateways using differentprotocols to communicate with one another. For example, an MME would nothave to communicate with a PDN. These devices were physically separate.In our virtualized embodiments, we connect with an MME and a PDN andother gateways in a virtualized manner. In the exemplary architecture ofFIG. 6, we mimic a traditional LTE eNodeB with S1-proxy 633 and we hosta TWAG 631 inside of the computing cloud 630. In the prior art, an ANDSFwould make an initial bearer assignment for a data packet or a voicepacket. This assignment was not dynamic, but rather was done as aninitial matter. In the computing cloud 630 architecture, we havecollocated a TWAG 631 and an S1-gateway, which is part of S1-proxy 633.This colocation allows us to make dynamic assignments, and mostimportantly reassignments of bearers on a variety of networks.

In addition, deep-packet inspection could be used to aggregate data in a“big data” sort of way. By this, we mean, data aggregation that can beused independent of a wireless communication network. One example of howbig data can be used is to bookmark societal trends for regionalmarketing and the like.

In addition to deep-packet inspection, the computing cloud 630 can alsoperform content-caching. In this embodiment, the computing cloud 630could determine the ten most popular YouTube clips on a real-time basis.The computing cloud 630 could use this determination and its contentcaching function to save local copies of these ten most popular YouTubevideos on local content server 655. Those of skill in the art willrecognize that computing cloud 630 could make this determination bynoting the web traffic of UEs within its coverage area or within alarger area to the extent that the network has been scaled to includemultiple computing clouds 630. Similarly, those of skill in the art willrecognize that this type of content caching could apply to manydifferent types of content including television shows, news articles,books, or any online content.

The computing cloud 630 also acts as a security gateway. It securesnetwork traffic to the MME gateway and the S1-gateway by providingstandard security protocols such as a VPN concentrator. Moreover, thecomputing cloud 630 has the intelligence to provide location-basedservice, such as weather, traffic, news, and the like.

The computing cloud 630 serves the function of pooling the resources ofthe virtualized wireless communication network 600. Each of themulti-RAT nodes 612, 614, 616 includes hardware for multiple radiotechnologies. The resources of the multi-RAT nodes are pooled so thatthe computing cloud 630 can efficiently allocate those resources in areal-time, dynamic fashion. All of this decision making and pooling ofresources goes on behind the scenes, so to speak; and the core network640 is unaware of how resources are allocated or that there is even morethan one base station sitting behind the computing cloud 630. In someembodiments, the determination of how to most efficiently allocate radioresources can be accomplished using self-organizing, self-optimizing,self-healing methods known as “SON,” and described more fully in U.S.patent application Ser. No. 14/024,717, entitled “HeterogeneousSelf-Organizing Network for Access and Backhaul,” filed on Sep. 12,2013, the entire contents of which are hereby incorporated by reference.

The computing cloud 630 continually monitors environmental conditionssuch as signal-to-noise ratio, latency, etc. within the virtualizedwireless communication network 600. It has a stored list of operatingparameters, such as the various power levels of each multi-RAT node 612,614, 616 the routing tables being used for data, the operationalprotocols and frequencies being used by each multi-RAT node 612, 614,616 which can be altered dynamically to account for less than optimalenvironmental conditions such as high signal to noise ratio, highlatency, or quality-of-service measures falling below a certainthreshold. These environmental conditions are merely illustrative andnot intended to be limiting.

The cloud component 630 can obtain myriad environmental conditionmeasurements. Some of the environmental conditions regarding aheterogeneous mesh network include: an interference measurement, acapacity measurement, a spectrum efficiency measurement, routing path, anetwork congestion measurement, a throughput measurement, a latencymeasurement, a coverage gap, a signal-to-noise ratio, aquality-of-service measurement, a radio-bearer utilization value, anavailable portion of spectrum, a load balancing measurement, status ofan operating heterogeneous mesh network, status of a multi-RAT nodewithin the heterogeneous mesh network, identifying information regardinga multi-RAT node, status of a wired connection within the heterogeneousmesh network, a frequency restriction, a signal strength measurement ofneighboring multi-RAT node, a request to join the heterogeneous meshnetwork, or the existence of a hidden node, and the like. Thus, unlikeprior art virtualized node hardware, environmental conditionmeasurements from multiple nodes within the virtualized communicationnetwork may be brought together in the cloud component, analyzed, andthen be used to notify, control, adjust, or reconfigure one, more thanone, or all consumers of the virtualized communication network.

In an alternate embodiment, the SON modules described herein could beharmonized with an external third-party network having its own set ofenvironmental conditions and operational parameters. These third-partyenvironmental conditions or third-party operational parameters could beany of the environmental conditions or operational parameters describedherein with respect to the SON network. In these embodiments, anXML-based interface could facilitate communication between a computingcloud server or a multi-RAT node containing SON modules described hereinand the third party network. When the SON module receives a third-partyenvironmental condition or a third-party operational parameter, such asan operating frequency, it can adjust an operational parameter withinits own internal network, for example, by altering the operatingfrequency of a multi-RAT node that may be experiencing interferencerelated to its proximity to the third-party network. The harmonizationbetween the SON networks described herein and third-party networks couldserve to greater utilize resources between both the SON networks and thethird-party networks by, for example, mitigating interference,coordinating handoffs, sharing unused spectrum, and the like.

Environmental conditions specific to multi-RAT nodes include: amulti-RAT node identification number, an identification number forsoftware stored in a multi-RAT node, a security parameter, a location ofa multi-RAT node, a configuration certificate of a multi-RAT node, anauthentication request, an operating frequency, or a handoff request,and the like.

Environmental conditions relating to specific user equipment beingserviced by multi-RAT nodes can also be measured and relayed tomulti-RAT nodes within the mesh network. Some of these environmentalconditions, which could also be processed by the disclosed SONembodiments, include: a range from a user equipment to a multi-RAT node,travel direction of a user equipment, a travel speed of a userequipment, a signal strength of a user equipment, a location of a userequipment, or a mapping application stored on a user equipment, anoperational channel, and the like.

The aforementioned environmental conditions could be measured by amulti-RAT node 512, 514, 516. They could be calculated within aprocessor on a multi-RAT node 512, 514, 516 or on a processor 532 in acomputing cloud 530. Similarly, they could be stored in cached memorywithin a multi-RAT node 512, 514, 516 or in a computing cloud 530 ordistributed throughout the network.

FIG. 7 illustrates the two-way nature of the virtualization ofembodiments of this invention. The first type of virtualization isvirtualization from the core network's perspective. FIG. 7 shows twowireless communication networks 710, 720 comprised of multi-RAT nodes722. Both of the wireless communication networks 710, 720 arecommunicatively coupled to the computing cloud 730. Although FIG. 7shows two separate connections to the computing cloud 730, communicationnetwork 710 could be communicatively coupled to communication network720, which in turn could provide the communicative coupling for bothnetworks to the computing cloud 730 in an alternate embodiment.Likewise, although FIG. 7 shows seven multi-RAT nodes, the number ofnodes could be as small as two and can have no upper limit.

For purposes of illustration, FIG. 7 includes two core networks 740,750. By way of example, one of the core networks 740 could be an AT&Tnetwork, and one of the core networks 750 could be a Verizon network.The virtualization methods and apparatuses disclosed herein create adual, or two-way virtualization as follows. From the AT&T core network's740 perspective, it sees its connection with the computing cloud 730 asa connection to a single base station. Similarly, from the Verizon corenetwork's 750 perspective, it sees its connection with the computingcloud 730 as a single connection to a single base station. Neither ofthe core networks 740, 750 is aware of the other.

The communication data within the network has been abstracted asdescribed more fully in U.S. patent application Ser. No. 13/889,631filed on May 8, 2013 and entitled “Heterogeneous Mesh Network and aMulti-RAT Node Used Therein.” As such, the data are agnostic withrespect to frequency, protocol, duplexing scheme and media accesstechnology. These abstracted data are not improperly multiplexed orcoded with header information to indicate to the EPC or core networkthat multiple different radio technologies are being serviced. Thesignaling that transpires between the computing cloud 730 and the corenetwork 740, 750 is the same type of signaling as would occur between abase station and the core network 740, 750 in some embodiments. Inalternative embodiments, the signaling between the computing cloud 730and the core network 740, 750 could be similar to the type of signalingthat occurs between a base station and the core network 740, 750, if forexample we were using an S1/S1′ proxy and customized proprietaryextensions between multi-RAT nodes and the S1/S1′ proxy.

The second type of virtualization is from an individual multi-RAT node's722 perspective. Multi-RAT node 722, which is part of a mesh network, iscommunicatively coupled to the computing cloud 730. The nature of a meshnetwork allows for dynamic adjustments to be made within the network.Computing cloud 730 oversees those dynamic adjustments by continuallyevaluating environmental conditions and operational parameters so as tobe able to make dynamic changes within the network. Some of thosechanges could, for example, be a change from an LTE access channel to awhite-space access channel in order for multi-RAT node 722 to improveits efficiency. In this situation, multi-RAT node 722 could beinstructed by computing cloud 730 to switch its access radio to a whitespace radio. The computing cloud 730 could instruct multi-RAT node 730as to which frequency to switch to within a white space sector. Beforereceiving this instruction, multi-RAT node 722 may have been unaware ofthe availability of a white space network, or which frequencies withinthe white space network were available. In that way, the networks towhich the computing cloud 730 are connected are virtualized from anygiven multi-RAT node's 722 perspective. Moreover, unlike prior artvirtualization methods, the frequencies within the white space networkneed not be dedicated or assigned exclusively to one consumer of thevirtualized resource.

In alternate method embodiments of the present invention, we havecreated computer implemented methods of virtualizing a network so thatthe core network believes that the virtualized network is a single basestation, when in fact the virtualized network is many base stations. Insome ways, this is the inverse of the typical virtualization—themulti-tenancy version of virtualization. The computer implemented stepsof the present invention could occur within a memory device stored onthe computing cloud 340, 530, 630, or 730 or in a distributed fashion.

FIG. 8 shows these steps. In the first step, the computing cloud 340,530, 630, or 730 pools 810 the radio resources of the multi-RAT nodeswithin a wireless communication network. In order to make a network,there has to be more than a single multi-RAT node actively participatingwithin the network. In some embodiments, the multi-RAT nodes could havethe same radio resources; and in other embodiments, their radioresources could be varied. For example, in one wireless communicationnetwork, there could be four multi-RAT nodes, each having a Wi-Fi radioand an LTE radio. The pooled resources would therefore be—four Wi-Firadios and four LTE radios. In a different embodiment having fourmulti-RAT nodes, the pooled resources could be: two TV white spaceradios; three LTE radios, two Wi-Fi radios, and a 3G radio. In either ofthese embodiments, the pooled resources can be used for either access orbackhaul.

In the next step of this method of virtualization, the computing cloud340, 530, 630, or 730 maintains 820 a connection with a core network oran upstream device. The computing cloud 340, 530, 630, or 730 couldestablish a connection with a core network or an upstream network deviceand then maintain that connection. It could maintain multipleconnections to multiple core networks or upstream devices as well.

The computing cloud 340, 530, 630, or 730 also in this method ofvirtualization manages 830 the pooled radio resources so that the corenetwork or upstream network device believes that it has a singleconnection to a base station. The core network or upstream networkdevices unaware of how many radio resources are being managed 830 by thecomputing cloud, of the fact that radio resources have been pooled, etc.In the past, radio networks, operator policies, service agreementsbetween service providers and their customers, differing operationalparameters, and the like have resulted in the partitioning andsegregation of radio resources. The virtualization embodiments describedherein address these shortcomings.

Stepping behind the curtain of virtualization, one of the goals of thecomputing cloud 340, 530, 630, or 730 is to efficiently manage all ofits pooled radio resources. This management can come into play at manydifferent communication lifecycles that occur within a communicationnetwork. There is the establishment of a call or data connection. Thereis the continued maintenance of in-progress call or data session. Thereare handover decision, paging requests, backhaul requirements. There areenvironmental conditions as discussed above that continually affectnetwork performance. There are equipment failures that affectperformance. The computing cloud 340, 530, 630, or 730 takes thesefactors into consideration when it manages 830 the pooled radioresources.

In terms of the management of the radio resources, this management couldbe performed in several different ways. In one embodiment, a completevirtualization embodiment, multiple operators can completely share allthe radio resources available. Radio resources are not reserved for anyoperator. Any subscriber of any operator may use all available radioresources at any time. In a second embodiment, split virtualization, thecomputing cloud 340, 530, 630, or 730 could store in a memory a list ofdedicated radio resources reserved for each operator. If the radioresources dedicated for a particular operator are fully utilized, then anetwork attachment or a connection request from a subscriber belongingto that operator will be denied.

In a third embodiment, fair access virtualization, the computing cloud340, 530, 630, or 730 could store in a memory a list of dedicated radioresources reserved for specific operators and a pool of unreserved radioresources. In this embodiment, if the radio resources for a particularoperator were fully utilized, the computing cloud 340, 530, 630, or 730could provide a radio resource for that operator from the pool ofunreserved radio resources. In a fourth embodiment, unbalancedvirtualization, the computing cloud 340, 530, 630, or 730 could providededicated radio resources to a subset of operators while also providinga pool of unreserved radio resources for the same or a different subsetof operators, or for all operators.

In additional embodiments, the computing cloud 340, 530, 630, or 730could manage 830 pooled radio resources by allocating a radio bearer inlight of a quality of service value, a radio bearer resource utilizationvalue, or other data priority metrics such as a data priority value, adata type, contractual relationships with network operators, or aservice level agreement.

Call admission control may determine if a call request will be admittedor rejected based on the virtualized resources available per operator toguarantee the quality of service requested. If a call is accepted, thequality of service of existing calls may be maintained. Call admissioncontrol may use the virtualization embodiments described herein to admitor reject new calls.

Additional embodiments of the systems disclosed herein could includelayered virtualization. In these embodiments, the basic architectureshown in FIG. 3 could be repeated N number of times, creating a total ofN computing cloud servers 340, 530, 630, or 730. These N computing cloudservers 340, 530, 630, or 730 could include modules stored in internalmemory that virtualize the total number of computing cloud servers,making it appear as though there is only one point of contact with theEPC, which appears to be an eNodeB or a base station. One advantage ofthese system embodiments is, from the core network's perspective, thereis only one connection that requires management of metrics such ashandoffs, paging, QoS measurements, billing, and the like. Additionally,using multiple clouds, on different networks, creates virtualredundancy. The resulting efficiencies include frequency reusability,maximization of network resources across multiple radio technologies,significant reduction in core management overhead, and the like.

The embodiments described herein reduce the system costs for handoversbecause the computing cloud 340, 530, 630, or 730 optimizes handoversbetween the pooled radio technologies. The computing cloud 340, 530,630, or 730 keeps track of all handovers for each of the multi-RAT nodeswithin its network. When a UE moves from the coverage area of onemulti-RAT node to another, the computing cloud 340, 530, 630, or 730absorbs the mobility on the handoff side by sending all packets destinedfor that UE to the new multi-RAT node that is servicing the UE. Thesepackets could include anchor data. Specifically, the computing cloud340, 530, 630, or 730 replaces the traditional function of the MME withrespect to the signaling between an MME and a traditional base station.

The multi-RAT nodes 612, 614, 616 of the embodiments described hereincoordinate with the computing cloud 630 to perform handovers of UEs in away that is invisible to the MME. These same benefits can be realized innetworks that have been scaled to include large numbers of multi-RATnodes or multiple computing clouds. By taking signaling to the MME outof the handoff scenario, the data traffic to the MME, and therefore overthe core network, is greatly reduced. The computing cloud 630 has aglobal view of the loading experienced by each multi-RAT node 612, 614,616 to which it is communicatively coupled. This global view allows thecomputing cloud 630 to achieve an optimal network load by selectivelyoffloading cell edge UEs to neighbor cells by intelligent redirection aswell as cell radius planning. Further, the computing cloud 630 can useTWAG activation to release LTE context in the MME and in a multi-RATnode 612, 614, 616. In these embodiments, the EPC does not have to beinformed every time a handoff occurs, thereby greatly reducing theoperational costs of the network.

The virtualization embodiments described herein also reduce operationalcosts of the network in terms of paging. Presently, when a UE is in idlemode and the core network must reach the UE, for example to connect acall, the core network sends a page to the UE. The core networktypically sends a paging message to the base station last servicing theUE and perhaps to other base stations within the same tracking area asthe UE's last active base station. If the UE cannot be located withinclose proximity to its last known location, the core network will startto broaden its search for the UE sending paging messages to a wider andwider audience of base stations.

These virtualization methods and systems lend themselves to a moreprecise way of locating a UE. In the virtualized embodiments describedherein, the computing cloud 340, 530, 630, or 730 has more informationabout any particular UE than the core network or an upstream networkdevice might have at any given moment in time. The computing cloud 340,530, 630, or 730, because it is an aggregator node that sits between amulti-RAT mesh network and the core network, is privy to informationrelated to any given UE's activities on other networks, such as Wi-Finetworks. If, for example, a particular UE was in IDLE mode on an LTEnetwork, but was actively downloading content from a local server via aWi-Fi connection, the computing cloud 340, 530, 630, or 730 would knowwhen a page came in for that UE where it was located and which multi-RATnode should be paged. In this example, the computing cloud 340, 530,630, or 730 could include a TWAG, which would enable it to use Wi-Fiattachment knowledge to more quickly locate a given UE. Similarly, if aUE was driving and it was using its GPS for navigation purposes, thecomputing cloud 340, 530, 630, or 730 would know where that UE was atany given moment. This type of heuristics paging could further reducethe tax placed upon the core network because the uncertainty inherenthaving the core network locate a UE in idle mode is reduced.

In the above-described embodiments, an SDN controller could becommunicatively coupled to the computing cloud 340, 530, 630, or 730server. In these embodiments, the SDN controller could push SDN policiesinto the virtualized network. The SDN controller could optionally becommunicatively coupled to an external SDN controller, thereby actinglike an SDN gateway to the virtualized network. In this embodiment, theSDN gateway could be implemented in proxy mode, passing along allcontrol information to SDN controlled devices, or in hybrid mode,implementing some of its own policies on the control information itreceives. In these SDN embodiments, the SDN controller(s) would also bevirtualized from the perspective of the core network.

The foregoing discussion discloses and describes merely exemplaryembodiments of the present invention. In additional embodiments, themethods described herein can be stored on a computer readable mediumsuch as a computer memory storage, a compact disk (CD), flash drive,optical drive, or the like. Further, the computer readable medium couldbe distributed across memory storage devices within multiple servers,multi-RAT nodes, controllers, computing cloud components, and the like.As will be understood by those skilled in the art, the present inventionmay be embodied in other specific forms without departing from thespirit or essential characteristics thereof. For example, wirelessnetwork topology can also apply to wired networks, optical networks, andthe like. Various components in the devices described herein may beadded, removed, or substituted with those having the same or similarfunctionality. Various steps as described in the figures andspecification may be added or removed from the processes describedherein, and the steps described may be performed in an alternativeorder, consistent with the spirit of the invention. Accordingly, thedisclosure of the present invention is intended to be illustrative, butnot limiting of the scope of the invention, as well as other claims. Thedisclosure, including any readily discernible variants of the teachingsherein, defines, in part, the scope of the foregoing claim terminology.

What is claimed is:
 1. A virtualized wireless communication networkcomprising a heterogeneous multi-RAT mesh network communicativelycoupled to a computing cloud component, the computing cloud componentfurther comprising a general purpose processor and an accelerator. 2.The virtualized wireless communication network of claim 1 wherein thecomputing cloud component acts as an S1-GW proxy and a WAG/MAG and an Iuproxy to facilitate LTE to Wi-Fi handover.
 3. The virtualized wirelesscommunication network of claim 1 wherein the computing cloud componentfurther comprises a memory for storing GTP-U data that can be used in anX2 handover.
 4. The virtualized wireless communication network of claim1 further comprising an SDN controller.
 5. The virtualized wirelesscommunication network of claim 1 wherein the computing cloud componentfurther comprises a memory for storing computer executable code thatwhen run causes the processor to update a network topology and aresource adjustment based on a capacity requirement of the heterogeneousmulti-RAT mesh network.
 6. The virtualized wireless communicationnetwork of claim 1 wherein the computing cloud component furthercomprises a memory for storing a GPS location of a heterogeneousmulti-RAT node, which is a part of the heterogeneous multi-RAT meshnetwork, the GPS location being analyzed by the computing cloudcomponent to determine if the heterogeneous multi-RAT node shouldconnect to a second computing cloud component in order to receive abetter quality of service or a lower data path latency.
 7. Thevirtualized wireless communication network of claim 1 further comprisinga database communicatively coupled to the computing cloud componentwherein the database stores a plurality of environmental conditions ofthe heterogeneous multi-RAT mesh network.
 8. The virtualized wirelesscommunication network of claim 1 wherein the computing cloud componentis communicatively coupled to an upstream network device and thecomputing cloud component further comprises a memory for storingcomputer executable code that when run causes the computing cloudcomponent to perform a calculation associated with allocation ofresources within the heterogeneous multi-RAT mesh network to theupstream network device.
 9. The virtualized wireless communicationnetwork of claim 8 wherein the upstream network device is an MME, a SGWor a PGW.
 10. The virtualized wireless communication network of claim 1wherein the computing cloud component is communicatively coupled to anupstream network device and the computing cloud component is virtualizedso that it appears to the upstream network device as a base station. 11.The virtualized wireless communication network of claim 10 wherein theupstream network device is an MME, an SGW, or a macro cell.
 12. Thevirtualized wireless communication network of claim 11 wherein the basestation is an eNodeB.
 13. The virtualized wireless communication networkof claim 1 wherein the heterogeneous multi-RAT mesh network furthercomprises a non-meshed backhaul connection to the computing cloudcomponent.
 14. The virtualized wireless communication network of claim13 wherein the backhaul connection is a wired connection.
 15. Thevirtualized wireless communication network of claim 13 wherein thebackhaul connection is a wireless connection.
 16. A computer implementedmethod of virtualization comprising the steps of a. Pooling a firstradio resource of a first multi-RAT node with a second radio resource ofa second multi-RAT node wherein the first and second multi-RAT nodes arecommunicatively coupled so as to form a mesh network; b. Maintaining aconnection to an upstream network device; and c. Managing the pooledresources so that the upstream network device interfaces with the pooledresources as a single base station.
 17. The computer implemented methodof virtualization of claim 16 further comprising storing anchor data ina memory device.
 18. The computer implemented method of virtualizationof claim 16 further comprising choosing the first radio resource or thesecond radio resource from the pooled resources to perform acommunication task based on an environmental condition.
 19. The computerimplemented method of virtualization of claim 16 further comprisinghanding off a data transmission from the first radio resource to thesecond radio resource.
 20. The computer implemented method ofvirtualization of claim 18 wherein the handing off of a datatransmission from a first radio resource to a second radio resource isbased upon an environmental condition.
 21. The computer implementedmethod of virtualization of claim 16 wherein the managing the pooledresources further comprises allocating a radio bearer by analyzing aquality of service value, a radio resource utilization value, or a datapriority metric.
 22. The computer implemented method of claim 16 furthercomprising: a. dynamically inspecting a data packet; b. determining adata type of the data packet; and c. choosing the first or second radioresource based on the data type.
 23. The computer implemented method ofclaim 16 further comprising caching content on a local content serverbased on a characteristic of data traffic within the network.
 24. Acomputer implemented method of paging a user equipment “UE” within aheterogeneous multi-RAT mesh network comprising the steps of: a.receiving a location request for a UE from a first network; b. using anetwork attachment information for a second network to locate the UE;and c. sending a paging message to the UE based on the networkattachment information for the second network.
 25. The computerimplemented method of claim 24 wherein the second network is a Wi-Finetwork and the network attachment information is Wi-Fi networkattachment information.